Systems and methods for generating a model for income scoring

ABSTRACT

Systems and methods consistent with the present invention generate a model for providing one or more scores indicating a likelihood that a stated income is faulty. In one embodiment, the method includes, for example, receiving information representative of at least one borrower; receiving a first income value for the at least one borrower; and receiving a second value for the at least one borrower, such that the second income value verifies the first income value. Moreover, the method includes determining one or more parameters for the model based on the received information, the received first income value, and the received second income value, such that the one or more parameters enable the model to provide the one or more scores.

BACKGROUND OF THE INVENTION

I. Field of the Invention

The present invention generally relates to financial systems and tosystems and methods for processing financial information. Moreparticularly, the invention relates to systems and methods forprocessing financial information, such that the processing determines amodel capable of providing an indication of the likelihood that areported or stated income is likely to be faulty.

II. Background and Material Information

When a borrower applies for a loan, the borrower may complete a loanapplication that includes, among other things, the borrower's income.This self-reported (or stated) income represents the borrower's currentincome, which will be used by the lender to process the loan.

When the lender subsequently processes the borrower's loan application,the lender may not have ready access to information that verifies theborrower's stated income. If the lender seeks to process the loanrapidly, which is usually the case, for example, in the Internetenvironment, the lender may process the loan and make a decision toapprove or reject the loan based on the income stated by the borrower onthe loan application. Also, mortgage lenders may have mortgage loanprograms with few, if any, documentation requirements. These low (or no)documentation loan programs may use a borrower's stated income in theirlending decision. Moreover, these mortgage programs may need to make an“on-the-spot” decision to approve a mortgage application while theborrower is present without requiring the borrower to return home tosearch for tax returns, pay stubs, or W-2s. As a result, the lender hasan interest in determining quickly and cheaply the veracity of income,whether self-reported by the borrower or reported by another entity as avalid representation of income

SUMMARY OF THE INVENTION

Accordingly, the present invention is directed to systems and methodsfor processing financial information and, more particularly, theinvention relates to systems and methods for processing financialinformation, such that the processing determines a model capable ofproviding an indication of the likelihood that a reported or statedincome is likely to be faulty.

A financial system consistent with the systems and methods of thepresent invention may generate a model for providing one or more scoresindicating a likelihood that a stated income is faulty. The financialsystem may receive information representative of at least one borrower.Moreover, the system may receive a first income value for the at leastone borrower and receive a second value for the at least one borrower.The second income value may verify the first income value. In addition,the system may determine one or more parameters for the model based onthe received information, the received first income value, and thereceived second income value, such that the one or more parametersenable the model to provide the one or more scores.

Additional features and advantages of the invention will be set forth inpart in the description that follows and in part will be obvious fromthe description, or may be learned by practice of the invention. Theobjectives and advantages of the invention may be realized and attainedby the system and method particularly described in the writtendescription and claims hereof as well as the appended drawings.

To achieve these and other advantages and in accordance with the purposeof the invention, as embodied and broadly described herein, methodsconsistent with the present invention may also provide a scoreindicating that a stated income for a borrower is likely to be faulty.The method also includes, for example, receiving informationrepresentative of the stated income; and determining the score based onthe received information and a model, such that the score indicates alikelihood that the stated income is faulty.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as described. Further featuresor variations may be provided in addition to those set forth herein. Forexample, the present invention may be directed to various combinationsand subcombinations of the disclosed features and/or combinations andsubcombinations of several further features disclosed below in thedetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various embodiments and aspectsof the present invention and, together with the description, explain theprinciples of the invention. In the drawings:

FIG. 1 illustrates an exemplary system environment in accordance withsystems and methods consistent with the present invention;

FIG. 2 is an exemplary block diagram for providing an indication that astated income is likely to be faulty based on a model in accordance withsystems and methods consistent with the present invention;

FIG. 3 is an exemplary flowchart for providing an Income Score based onreceived information and a model in accordance with systems and methodsconsistent with the present invention;

FIG. 4 is an exemplary flowchart for generating a model in accordancewith systems and methods consistent with the present invention;

FIG. 5 illustrates another exemplary system environment in accordancewith systems and methods consistent with the present invention;

FIG. 6 is another exemplary flowchart for providing an Income Score inaccordance with systems and methods consistent with the presentinvention;

FIG. 7 illustrates exemplary information used to determine a borrower'sIncome Score in accordance with systems and methods consistent with thepresent invention;

FIG. 8 depicts an exemplary web page interface for providing informationin accordance with systems and methods consistent with the presentinvention;

FIG. 9 shows an exemplary model for determining an Income Scoreindicating the likelihood that a stated income is likely to be faulty inaccordance with systems and methods consistent with the presentinvention;

FIG. 10 depicts an exemplary web page interface for receiving an IncomeScore in accordance with systems and methods consistent with the presentinvention;

FIG. 11 is another exemplary flowchart for generating a model inaccordance with systems and methods consistent with the presentinvention;

FIG. 12 is an exemplary flowchart for determining model coefficients inaccordance with systems and methods consistent with the presentinvention; and

FIG. 13 shows an exemplary table for determining coefficients of themodel in accordance with systems and methods consistent with the presentinvention.

DETAILED DESCRIPTION

Reference will now be made in detail to the invention, examples of whichare illustrated in the accompanying drawings. Wherever possible, thesame reference numbers will be used throughout the drawings to refer tothe same or like parts.

Systems and methods consistent with the present invention permit afinancial entity, using a computing platform (or computer), to determinean indication of whether a stated income or reported income for aborrower is likely to be faulty. In one embodiment, the financial entitymay determine such indication in the form of an Income Score.

In one embodiment, a stated (or reported) income that is faulty wouldinclude one that does not accurately represent the borrower's actualincome, such as a stated income that is either higher or lower than theborrower's actual income. Stated income, as used herein, means any valuethat represents (or claims to represent) an actual income amount. Anactual income amount, as used herein, means any value that represents aborrower's true income. Although stated income may be self-reported by aborrower, a skilled artisan would recognize that any entity may reportthe borrower's purported income instead.

In one aspect of the invention, the computing platform determines theIncome Score based on a model and received income information, such asthe borrower's stated income. The computing platform then scales theIncome Score into a range (e.g., 300 to 900) with a low score indicatinga high likelihood that the stated income is faulty (i.e., the statedincome is probably false) and a high score indicating a low likelihoodthat the stated income is faulty (i.e., the stated income is probablytrue). Alternatively, a high score could be used to indicate a highlikelihood that the stated income is false (e.g., stated income is toohigh or too low when compared to the borrower's actual income).

By way of example only, a borrower may include an income on anapplication, such as a loan application, credit card application, ormortgage application. A financial entity, such as a lender, bank,mortgage bank, or mortgage broker, may process the borrower'sapplication based on, among other things, the income stated on theborrower's application. If the borrower's stated income qualifies theborrower for the loan, the financial entity may approve the loan.Moreover, by using the borrower's stated income, the financial entitymay quickly approve (or reject) the loan amount or line of credit. Thefinancial entity thus has an interest in assessing the likelihood ofwhether the borrower's stated income is faulty. Otherwise, the financialentity may incorrectly approve or reject the loan or credit line.

By using the Income Score, the financial entity may determine thelikelihood that the borrower's stated income is faulty. When the IncomeScore indicates a low likelihood that the stated income is faulty, thelender may simply process the loan application to approve or reject theloan because the borrower's stated income is probably true (e.g., statedincome is about equal to, or lower than, the borrower's true income). Onthe other hand, when the Income Score indicates a high likelihood thatthe stated income is faulty, the lender may take additional measuresbefore approving or rejecting the loan because the stated income isprobably false (e.g., stated income is higher than the borrower's trueincome). Such measures may include verifying the borrower's income byrequesting additional income information from the borrower, theborrower's employer, and/or a financial database capable of verifyingthe borrower's stated income.

The following detailed description refers to mortgage loans tofacilitate explanation, and such references should in no way beconstrued to limit the systems and methods described herein.

FIG. 1 shows an exemplary system 1000 for providing an indication, ofwhether a stated or reported income is likely to be faulty. Theindication may be in the form of an Income Score, enabling a financialentity to readily determine the likelihood that the borrower's income isfaulty.

Referring to FIG. 1, the system includes a communication channel 1400,one or more lenders 1500, 1510, one or more borrowers 1600, 1610, one ormore brokers 1700, 1710, one or more information sources 1800, and aprocessor 1350. The lenders 1500, 1510 may include a financial entity,such as a bank, mortgage bank, mortgage broker, mortgage originator, andany other entity seeking an indication of whether the borrower's statedincome is likely to be faulty. The borrowers 1600, 1610 may include anentity, such as a consumer, seeking a mortgage. The brokers 1700, 1710may include an entity that acts as an agent, such as a mortgage broker.The processor 1350 may include an entity capable of processinginformation such that an Income Score is provided to, for example, thelender, borrower, broker, and any other entity requesting the IncomeScore. The information sources 1800 may include internal, external,proprietary, and/or public databases, such as financial databases anddemographic databases.

In one embodiment, the information sources 1800 may include informationfrom one or more of the following: International Data Management Inc.(IDM), First American Corporation (First American), other property dataproviders, county property (or tax) records, TransUnion LLC(TransUnion), Equifax Inc. (Equifax), Experian, Department of Commerce,Internal Revenue Service (IRS) statistics on income, and Bureau of LaborStatistics. Moreover, the information from the Bureau of LaborStatistics may include detailed salary statistics based on one or moreof the following: occupation, level of experience, and region, such ascity, state, or Metropolitan Statistical Area (MSA). For example, theBureau of Labor Statistics may include income estimates for hundreds ofoccupations and professions.

Although the communication channel 1400 is depicted in FIG. 1 asbi-directional, a skilled artisan would recognize that unidirectionalcommunication links may be used instead.

FIG. 2 depicts a functional block diagram associated with providing theIncome Score consistent with the systems and methods of the presentinvention. FIG. 2 depicts a model 220, serving as a mathematical modelor transform, that determines a score 230. The score provides anindication of the likelihood that the borrower's stated income is faulty(or false). For example, a financial entity or information source mayprovide the processor 1350 with information, such as one or more of thefollowing: information describing the borrower 205; loan (orapplication) information 215; information describing the borrower'sincome 216, such as the income reported by the borrower to the lender ona mortgage loan application; credit information 217; propertyinformation 218; and demographic information 219 (e.g., salarystatistics). The processor 1350 may then use the model 220 to determinethe Income Score and provide the score 230 to the lender 1500 via thecommunication channel 1400. Accordingly, the lender 1500 may moreaccurately evaluate the borrower's income when processing the mortgageloan application.

FIG. 3 is an exemplary flowchart depicting steps for providing theIncome Score. Referring to FIGS. 1 and 3, in one embodiment, theprocessor 1350 may begin by receiving information through thecommunication channel 1400 from the lender 1500 (steps 3005-3100). Thereceived information may include, for example, information describingthe borrower and the borrower's income. In one aspect of the invention,the information describing the borrower may include one or more of thefollowing: the borrower's name, address, occupation, years in occupation(or experience), and Social Security Number (SSN). Furthermore, theborrower's income information may include information reported by theborrower to the lender, such as the borrower's current annual incomeand/or the borrower's household income.

In one embodiment, the received information may also include detailedcredit information for the borrower, such as a credit history. Forexample, detailed credit information for the borrower may be receivedfrom a lender and/or a credit repository such as TransUnion, Equifax, orExperian. When the detailed credit information is received from a creditrepository, that information may include one or more of the following:number of accounts in the credit report, account balances in revolvingcredit lines, account limits in revolving credit lines, other accountbalances, mortgage balances, mortgage payments, tax payments, 30 daydelinquencies, 60 or 90 day delinquencies, foreclosures, and otherinformation reported by credit repositories. Moreover, the processor1350 may also receive information, such as salary estimates andstatistics based on occupation, region, and experience, from publicsources, such as the Department of Commerce, IRS, or Bureau of LaborStatistics.

One of ordinary skill in the art would recognize that some of theinformation received in step 3100 may be received at any time (andstored) or received when a request is made by a lender for an IncomeScore. For example, before a specific request for a borrower's incomescore is made by a lender, information, such as salary estimates andstatistics based on occupation, may be received and stored.

The processor 1350 may then determine the Income Score based on thereceived information (and/or stored information, if any) and the model220 (step 3200). The processor 1350 may also provide the Income Score(step 3300) to the lender 1500 through the communication channel 1400.The lender 1500 may then use the Income Score to determine whether theborrower's income, as stated on the borrower's loan application, islikely to be faulty.

In one embodiment, the stated income is considered faulty when itdiffers significantly from the borrower's actual income. For example, ifa borrower earns $20,400 dollars per year and reports $20,000, theborrower's stated income, although false, is not worrisome because theborrower has merely understated income, which results in no harm to thelender. In this example, the Income Score would not necessarilycorrespond to a low score indicating a false stated income. On the otherhand, if a borrower earns $20,400 and reports $24,000 on a loanapplication, the stated income represents a worrisome exaggeration ofthe borrower's actual income. In this case, the Income Score wouldcorrespond to a low score.

When the Income Score is low (e.g., the exaggerated stated incomeexample above), the lender may decide to review the borrower'sapplication and verify the borrower's stated income. On the other hand,if the borrower's stated income in unlikely to be exaggerated (i.e.,probably true), the lender may simply approve or reject the loan withoutfurther verifying the borrower's income.

In one embodiment, the processor 1350 may scale an Income Score suchthat the score falls within a range, such as 300-900. Table 1 belowshows three exemplary Income Scores with a likelihood that theborrower's stated income is faulty and a proposed action for the lender1500. For example, when the processor 1350 provides an Income Score of500 to the lender 1500, the Income Score may indicate that theborrower's stated income is highly likely to be unreliable (or false).With the Income Score of 500, the lender 1500 may conduct a detailedreview of the loan application including verifying the borrower's incomeby requesting income verification information, such as requesting paystubs from the borrower, calling the borrower's employer, or requestinginformation from a credit bureau that lists income information for theborrower.

When the processor 1350 provides an Income Score of 600 to the lender1500, the Income Score of 600 may indicate that the borrower's statedincome is somewhat less likely to be unreliable than the 500 score. Thelender 1500 may still conclude that a review of the borrower's loanapplication is appropriate. But in this case, the review may merelyinclude verifying the borrower's stated income based on phone calls toemployers or other income verification measures.

When the processor 1350 provides an Income Score of 700 to the lender1500, the Income Score of 700 may indicate that the borrower's statedincome is likely to be more reliable than the 600 score. In this case,the lender 1500 may be sufficiently confident that the income stated onthe borrower's loan application is reliable. Accordingly, the lender1500 may approve the loan based on the borrower's stated income withoutfurther verifying the stated income.

TABLE 1 Exemplary Income Scores INCOME SCORE FOR A LIKELIHOOD OFBORROWER FAULTY INCOME PROPOSED ACTION 500 High Conduct a detailedreview that verifies the borrower's income, such as requesting paystatements. 600 Medium Conduct a less detailed review, such as merelycalling the borrower's employer, or, alternatively, decrease theimportance of the borrower's stated income when approving the loan. 700Low Process the loan using the borrower's stated income.

Although Table 1 shows three Income Scores between 500 and 700, anyother range of Income Scores may be used instead including, for example,a range of Income Scores from 1 to 10 or 300 to 900. Alternatively, analphabet-based or alphanumeric-based scale may also be used instead. Forexample, an alphabet-based approach may include a range of “A” to “D”,while an alphanumeric-based approach may include a range of “A1” to“A10.” Moreover, although Table 1 shows a higher score representing thatthe borrower's stated income is probably reliable (or good), a skilledartisan would recognize that a lower score could instead represent thatthe stated income is probably reliable.

In one embodiment, the Income Score may correspond to the combinedIncome Score for multiple borrowers. For example, when multipleborrowers (each with a corresponding individual score) apply for asingle mortgage loan, the processor 1350 may combine the scores todetermine a combined Income Score. The combined Income Score may then bescaled to fall within a range, such as 300-900. In this embodiment, theIncome Score represent the veracity of the income (or combined income)of the borrowers.

FIG. 4 is an exemplary flowchart depicting steps for generating a model,such as the Income Score model, capable of providing the Income Score.Referring to FIGS. 1 and 4, in one embodiment, the processor 1350 maybegin by receiving information from information sources 1800 to enablethe processor 1350 to generate the Income Score model (steps 4500-4505).The processor 1350 may then process the received information todetermine the Income Score model (step 4600); and provide the IncomeScore model to one or more entities (e.g., lenders 1500, 1510 and/orbrokers 1700, 1710), permitting those entities to determine (or use) theIncome Scores for mortgage loan applications (step 4700). The IncomeScore model may then be used as the model 220 depicted in FIG. 2 todetermine the score 230. Although the Income Score model is describedherein, a skilled artisan would recognize that any type of model thatprovides a score may be used instead.

In one aspect of the invention, the processor 1350 may periodically(e.g., yearly, monthly, etc.) determine whether the Income Score modelshould be updated and then perform steps 4500-4700 (step 4800).

FIG. 5 illustrates another exemplary system environment 5000 consistentwith the systems and methods of the present invention. As illustrated inFIG. 5, the system 5000 includes a processor 1350, lenders 1500, 1510,borrowers 1600, 1610, brokers 1700, 1710, information sources 1800, anda communication channel 1400. The processor 1350 may also include aninput module 5100, an output module 5200, a computing platform 5300, andone or more databases 5600.

In one embodiment consistent with FIG. 5, the computing platform 5300may include a data processor such as a PC, UNIX server, or mainframecomputer for performing various functions and operations. Computingplatform 5300 may be implemented, for example, by a general purposecomputer or data processor selectively activated or reconfigured by astored computer program, or may be a specially constructed computingplatform for carrying-out the features and operations disclosed herein.Moreover, computing platform 5300 may be implemented or provided with awide variety of components or systems including, for example, one ormore of the following: one or more central processing units, aco-processor, memory, registers, and other data processing devices andsubsystems.

Communication channel 1400 may include, alone or in any suitablecombination a telephony-based network, a local area network (LAN), awide area network (WAN), a dedicated intranet, the Internet, or awireless network. Further, any suitable combination of wired andwireless components and systems may be incorporated into thecommunication channel 1400. Although the computing platform 5300 mayconnect to the lenders 1500, 1510 through the communication channel1400, computing platform 5300 may connect directly to the lenders 1500,1510.

Computing platform 5300 also communicates with input module 5100 and/oroutput module 5200 using connections or communication links, asillustrated in FIG. 5. Alternatively, communication between computingplatform 5300 and input module 5100 (or output module 5200) may beachieved using a network (not shown) similar to that described above forthe communication channel 1400. A skilled artisan would recognize thatcomputing platform 5300 may be located in the same location or at ageographically separate location from input module 5100 or output module5200 by using dedicated communication links or a network.

Input module 5100 may be implemented with a wide variety of devices toreceive and/or provide information. Referring to FIG. 5, input module5100 may include an input device 5110, a storage device 5120, and anetwork interface 5130. Input device 5110 may also include a keyboard, amouse, a disk drive, a telephone, or any other suitable input device forreceiving and/or providing information to computing platform 5300.Although FIG. 5 only illustrates a single input module 5100, a pluralityof input modules 5100 may also be used.

Storage device 5120 may be implemented with a wide variety of systems,subsystems, and/or devices for providing storage (or memory) including,for example, one or more of the following: a read-only memory (ROM)device, a random access memory (RAM) device, a tape or disk drive, anoptical storage device, a magnetic storage device, a redundant array ofinexpensive disks (RAID), and/or any other device capable of providingstorage.

Network interface 5130 may facilitate data exchange between thecommunication channel 1400 and computing platform 5300 and may alsofacilitate data exchange between the input module 5100 and the computingplatform 5300. In one aspect of the invention, network interface 5130may permit a connection to at least one or more of the followingnetworks: an Ethernet network, an Internet protocol network, a telephonenetwork, a radio network, a cellular network, or any other networkcapable of being connected to input module 5100.

Output module 5200 may include a display 5210, a printer 5220, and anetwork interface 5230. The output module 5200 may be used to provide,inter alia, Income Scores to lenders 1500, 1510, provide an Income Scoremodel to the computing platform 5300, and/or provide the Income Scoremodel to any entity or processor. Further, the output from computingplatform 5300 may be viewed through display 5210 (e.g., a cathode raytube or liquid crystal display) and printer device 5220. For example,the Income Score may be viewed on display 5210 and on printer device5220. Although FIG. 5 only illustrates a single output module 5200, aplurality of spatially separated output modules 5200 may be usedinstead.

Network interface 5230 may facilitate data exchanges between the outputmodule 5200 and the computing platform 5300 and between the computingplatform 5300 and the communication channel 1400. In one embodiment, thenetwork interface 5230 may be similar to the network interface 5130described above.

The database 5600 may store information received from the lenders,brokers, borrowers, and/or information sources. For example, thedatabase 5600 may store information received from the informationsources 1800 such as information from one or more of the following: IDM,First American, other property data providers, county property (or taxrecords), TransUnion, Equifax, Experian, Department of Commerce, IRSstatistics on income, and Bureau of Labor Statistics. Although thedatabase 5600 is shown in FIG. 5 as being located with the computingplatform 5300, a skilled artisan would recognize that the database(s)may be located anywhere (and in multiple locations) and connected to thecomputing platform via direct links or networks. Similarly, althoughinformation sources are depicted in FIG. 5 as separate from thecomputing platform 5300 and processor 1350, a skilled artisan wouldrecognize that the information sources may be located anywhere (and inmultiple locations) and connected to the computing platform via directlinks or networks.

FIG. 6 shows another exemplary flowchart with steps for providing theIncome Score. Referring to FIGS. 5 and 6, the computing platform 5300may begin when it receives, via the communication channel 1400,information (steps 6005-6100). The received information may includeinformation provided by the borrower 1600 to the lender 1500, such asone or more of the following: the borrower's name, address, occupation,experience (or years in current position), stated income, and any otherinformation included on the borrower's loan application. In addition,the received information may include credit information from informationsources that provide credit history, such as First American, Equifax,TransUnion, or Experian. The received information may also include assetinformation, such as property and tax assessor information provided byFirst American, IDM, and other data providers; and demographicinformation, such as statistics detailing the borrower's estimatedincome based on the borrower's occupation, address, and/or experience.

The computing platform 5300 may initialize one or more variables for usein the Income Score model based on the received information (step 6200);determine the Income Score based on the Income Score model and receivedinformation (step 6300); and end when it provides the Income Score to,for example, the lender 1500 (steps 6400-6500).

To receive information (step 6100), the computing platform 5300 mayreceive from the lender 1500 information representative of the borrower.For example, the lender 1500 may provide the computing platform 5300with the information listed on the borrower's loan application such asthe borrower's name, address, occupation, experience (or years incurrent position), SSN, and stated income. In addition, the computingplatform may receive other information from other sources. For example,the received information may also include the borrower's credit historyreceived from credit information repositories; property (or asset)information, such as an address for the borrower's property and anappraised value for that property; and demographic information includingincome (or salary) information, such as estimates (or statistics) onincome received from the Bureau of Labor Statistics.

In one embodiment, the computing platform 5300 stores in database 5600income statistics based on, inter alia, income estimates (or statistics)from the Bureau of Labor Statistics. Moreover, the stored statisticsenable the computing platform 5300 to retrieve income information basedon demographics, such as the borrower address (e.g., city, state, orMSA), occupation, and years of experience. For example, the computingplatform 5300 may retrieve income statistics for a lawyer, with 20 yearsexperience, living in the Baltimore, Washington, and Virginia MSA. Theretrieved income statistic for that lawyer may indicate an averageincome of about $60,000 per year. The income statistic may also be usedas received information in step 6100.

In one embodiment, the computing platform 5300 may receive informationthrough the communication channel 1400. This received information mayinclude the information depicted in FIG. 7. Referring to FIG. 7, thereceived information may include one or more of the following: a loan(or mortgage) reference number; the identity of the requestor (e.g., thelender 1500); the borrower's identity (e.g., name); the borrower'sstated income; the borrower's address (e.g., street, city, state, andZIP code); the borrower's occupation and the years of experience in thatoccupation; an estimated value (or appraisal) for the borrower'sproperty (i.e., the borrower's current property and/or a property beingmortgaged); an amount corresponding to the total loan amount requestedby the borrower; and information indicating the property type (e.g., acondominium, a town house, a single family home, a 2-4 unit dwelling, ora multifamily dwelling). Moreover, the received information may includeinformation indicating the purpose of the mortgage, such as whether themortgage is for the purchase of a property, a mortgage refinancing, amortgage refinancing with cash returned to the borrower (referred to asa “cash out” refinance), a home improvement loan, a debt consolidationloan, or any other type of mortgage loan, line of credit, or otherfinancing.

The received information may also include one or more of the following:an indication of the borrower's credit worthiness, such as creditscores, credit history, delinquencies, outstanding balances on loans,and loan limits; a flag indicating the source of the borrower creditinformation (e.g., credit information provided by a lender or by acredit repository); a median price (or appraisal) for properties withina region or, alternatively, an estimated price for the borrower'sproperty (listed in FIG. 7 as a “Combined Point Value”).

The received information may also include demographic information, suchas income estimates (or statistics) based on the borrower's occupation,experience, and region, such as a street address, neighborhood, city,state, country, or MSA (depicted on FIG. 7 as “Income Estimate”).

Although the computing platform 5300 receives information primarily fromthe lender 1500, a skilled artisan would recognize that the computingplatform 5300 may receive such information from any source (or entity)including lenders 1500, 1510, brokers 1700, 1710, borrowers 1600, 1610,and/or information sources 1800.

Referring again to FIG. 6, when receiving information (step 6100), thecomputing platform 5300 may interface with, or be embedded in, one ormore systems (not shown), that provide financial information, creditinformation, or real estate information. Such systems include, forexample, systems that are used to originate loans or (pre)approve loansor credit cards.

In one embodiment, the information depicted in FIG. 7 may be providedvia a web-based input. FIG. 8 shows a web page for providing informationto the processor 1350 (or computing platform 5300) via the Internet. Alender, a borrower, a broker, or any other entity seeking an IncomeScore may access the web page of FIG. 8 to provide the processor 1350(or computing platform 5300) with information. The information providedvia the web page of FIG. 8 may then be used by the computing platform5300 to determine the Income Score.

Moreover, one of ordinary skill would recognize that the demographicinformation received by the computing platform 5300, such as incomestatistics (or estimates), may be stored in database 5600 and accessedwhen the computing platform 5300 receives borrower information,indicating a request for an Income Score.

Referring again to FIG. 6, to initialize variables for use in the IncomeScore model (step 6200), the computing platform 5300 may initialize oneor more of the variables in the Income Score model based on theinformation received in step 6100. For example, based on the receivedinformation, the computing platform may initialize the variable CO,which represents a cash-out mortgage loan, to a “1” based on whether theborrower's loan provides cash-out to the borrower.

In one embodiment, the computing platform 5300 may initialize thevariables listed in Table 2 below based on the information received instep 6100 (step 6200). Although Table 2 lists only six variables, one ofordinary skill would recognize that one or more variables may be usedincluding, for example, the variables listed in FIG. 7 or any othervariables that may suggest a borrower's veracity when self reportingincome. The variable CREDIT_SCORE corresponds to an indication of theborrower's credit worthiness. The variables NCO, CO, and OTH correspondto the purpose of the loan: NCO represents a loan without cash out tothe borrower; CO represents a loan with cash out to the borrower; andOTH represents any other loan purpose. The variable DELINQUENCIEScorresponds to the number of late or non-payments accumulated by theborrower. The variable INCOME_DIFF may be determined based on thefollowing equation:INCOME_DIFF=(log(stated income)−log(income estimate))/Std dev  Equation1where stated income is the borrower's reported income; income estimateis an estimate provided by an income estimation model; Std devrepresents the standard deviation associated with the income estimate;and log represents a natural logarithm.

TABLE 2 Initialized Variables CREDIT_SCORE = credit score expressed inintegers, e.g. 715. NCO = 1 if purpose is rate/term refinance, else 0.CO = 1 if purpose is cash out refinance, else 0. OTH = 1 if purpose isother, else 0. DELINQUENCIES = number of delinquencies in borrower'scredit report INCOME_DIFF = (log(stated income) − log(incomeestimate))/Std dev (Logs are natural logs.)

The income estimation model may correspond to published (or available)estimates of incomes (or salaries) based on occupation, experience, andregion. In this case, the income estimation model simply provides anestimate of a borrower's income based on the borrower's occupation,years of experience, and address. The estimate may be in the form of amedian (or mean) salary, i.e., hourly, weekly, monthly, or yearlysalary. Moreover, the model may also include a statistic indicating therelative error, such as a standard deviation, associated with the incomeestimation model.

In cases where income information is not published (or available) for aspecific occupation, region, and experience level, the income estimationmodel may perform a more complex estimation based on techniques such aslinear regression or other statistical procedure. Nonetheless, even whena more complex estimation technique is used, the income estimateprovided by the estimation model may still be in the form of a median(or mean) salary and a corresponding standard deviation associated withthe specific income estimation model used. A skilled artisan wouldrecognize that linear regression is known in the art and that softwaretools are commercially available to facilitate linear regressionmodeling. Moreover, income estimation models are also known andcommercially available. For example, the Bureau of Labor Statisticsprovides several income estimation models that estimate income based onvarious factors including occupation, area, and/or experience.

To determine the Income Score (step 6300 of FIG. 6), the computingplatform 5300 may determine the Income Score by multiplying thevariables initialized in step 6200 by its corresponding coefficients (orweights) from the Income Score model. The computing platform 5300 mayalso scale the Income Score into a predetermined range, such as therange of 300 to 900. The computing platform 5300 may then provide thescaled Income Score to the lender or other entity that requested theIncome Score (step 6400).

FIG. 9 shows an exemplary Income Score model. To determine the IncomeScore (step 6300), the computing platform 5300 may determine the productof each variable (e.g., CREDIT_SCORE) initialized in step 6200 and itscorresponding model coefficient (e.g., +1). The computing platform 5300may then sum all of the products to produce an Income Score (lines 1-7).

Moreover, in one aspect of the invention, the Income Score is scaledinto a range of 300 to 900 such that an Income Score of 300 suggeststhat a borrower's stated income is probably faulty. On the other hand,an Income Score of 900 would indicate that the stated income value isprobably reliable. FIG. 9 at lines 9-10 shows scaling the Income Scoreto 300 and 900 when the Income Score is less than 300 or greater than900, respectively.

FIG. 10 depicts an exemplary web page for providing an Income Score. Forexample, the computing platform 5300 may provide the Income Score to thelender 1500 through the communication channel 1400. As shown in FIG. 10,the Income Score may provide the lender 1500 with an indication ofwhether the borrower's income, as stated on the borrower's loanapplication, is likely to be faulty.

By way of example only, FIG. 10 depicts that an Income Score below 500may be considered at “highest risk” of being faulty, suggesting to thelender 1500 that a detailed review (or verification) of the borrower'sstated income may be appropriate. An Income Score between 500-600 may beconsidered at “moderate risk” of being faulty, suggesting to the lender1500 that an income verification based merely on credit bureauinformation may be appropriate. When an Income Score is above 700, theborrower's stated income is at “lowest risk” of being faulty, suggestingto the lender 1500 that no further verification of the borrower's statedincome is necessary.

In one embodiment, the computing platform 5300 may also generate theIncome Score model, as depicted in FIG. 11. The computing platform 5300may begin by receiving historical (or truth) information (steps11005-11100); determine the Income Score model based on the receivedhistorical information (step 11200); and end when it provides the IncomeScore model (steps 11300-11400).

The computing platform 5300 may receive historical information for oneor more borrowers from various sources of information, such as database5600 or the information sources 1800 (step 11100). The historicalinformation may include any information that might indicate the accuracyof a borrower's stated income including one or more of the following:property information, borrower information, loan information, incomeinformation, credit information, and demographic information. Thedemographic information may also describe one or more of the following:the borrower; the borrower's property; and any other demographics, suchincome estimates based on occupation, experience, and region (orlocation).

In one aspect of the invention, the historical information for eachborrower may also include the information depicted in FIG. 7 and incomeinformation that verifies the borrower's true (or actual) income. Theverified income information for each borrower may include incomeinformation that is considered reliable and, preferably, accurate.

To determine the Income Score model, the computing platform 5300 mayprocess the historical information received in step 11100 (step 11200).The received historical information may be processed based onquantitative techniques, such as statistics (e.g., logistic regressionand PROBIT), neural networks, and/or any other approach that provides amodel capable of providing an Income Score for a borrower's statedincome. For example, standard statistical tools, such as toolscommercially available from the SAS Institute, Inc., may be used todetermine the Income Score model coefficients based on the receivedhistorical information such that the Income Score model provides IncomeScores. The computing platform 5300 may then provide the determinedIncome Score model to one or more entities (e.g., lenders 1500, 1510and/or brokers 1700, 1710), permitting those entities to determine (oruse) the Income Scores.

Referring again to FIG. 9, the exemplary Income Score model listsseveral variables including CREDIT_SCORE, CO, NCO, OTH, DELINQUENCIES,and INCOME_DIFF. The coefficients 100, 1, −10, +10, 0, −30, and −100,000are determined based on the received historical information from step11100.

In one embodiment, the computing platform 5300 may use a statisticaltechnique referred to as logistic regression to determine thecoefficients of the Income Score model. Logistic regression models maybe used to examine how various factors influence a binary outcome. Anevent (or result) that has two possible outcomes is a binary outcome(e.g., good/bad or faulty/reliable). As noted above, logistic regressionmodeling is available with many commercially available statisticalsoftware packages.

FIG. 12 shows an exemplary flowchart depicting steps for using alogistic regression approach for determining the coefficients of theIncome Score model. The logistic regression approach permits determiningthe model coefficients using the historical information received in step11100.

Referring to FIG. 12, the computing platform 5300 may verify eachborrower's stated income (step 12100); determine an outcome (alsoreferred to as an outcome variable) for each borrower based on thehistorical information and the borrower's verified income (step 12200);determine the likelihood associated with possible outcomes (step 12300);determine coefficients for the Income Score model (step 12400); andadjust the coefficients by scaling the coefficients (or the estimatedlog odds/probability) into a range (step 12500).

To verify the borrower's stated income (step 12100), the computingplatform 5300 may compare the borrower's stated income to a verifiedincome value for the borrower. The verified income value for eachborrower may be received as part of the historical information.Moreover, the verified income value may include income verificationinformation provided by a credit bureau, by each borrower (e.g., paystubs and W-2s), by each borrower's employer, and/or by other sources ofreliable income information, such as loan, credit, and mortgageapplications.

Based on the comparison, the computing platform 5300 may verify whetherthe borrower's stated income is valid. For example, if the verifiedincome for a borrower is lower than the borrower's stated income, theborrower's stated income may be invalid. On the other hand, when theborrower's stated income is about equal (e.g., within 10%) to theborrower's verified income, the stated income may be considered valid.In one embodiment, the borrower's stated income is also considered validif it is less than the borrower's verified income because the borrowerhas harmlessly understated income.

FIG. 13 shows an exemplary table showing received historical informationfor borrowers including the borrower's stated income, a verified incomefor the borrower, and an outcome. FIG. 13 also shows that for eachborrower, the received historical information may include other borrowerrelated information, such as a credit score; a mortgage type, such ascash out (CO), non-cash out (NCO), or other (OTH) mortgage type;delinquency information indicating late or nonpayment history; and afactor (labeled in FIG. 13 as INCOME_DIFF) based on the differencebetween the borrower's stated income and an income estimate derived froman income estimation model, as described above with respect toEquation 1. A skilled artisan would recognize that additional historical(or truth) information may also be received by the computing platform5300 to determine the model coefficients including any other informationdescribing the borrower, the loan, the borrower's property, theborrower's income, the borrower's credit, and any other information thatmay facilitate determining whether the borrower's stated income islikely to be faulty.

To determine the outcome for each borrower (step 12200), the computingplatform 5300 may compare the verified income to the stated income. Ifthe verified income is lower than the stated income, the computingplatform 5300 may set the outcome to a “1,” suggesting that the statedincome is invalid. On the other hand, if the verified income is aboutequal to or greater than the stated income, the computing platform 5300may set the outcome to “0,” suggesting that the stated income is valid.Referring again to FIG. 13, the computing platform 5300 thus processesthe historical information for each borrower to determine an outcomebased on the borrower's stated income and verified income, storing theinformation depicted in FIG. 13 in the database 5600.

To determine the likelihood for each of the possible outcomes (step12300), the computing platform 5300 may further process the historicalinformation, using a logistic regression, to determine the odds that anoutcome is possible. For example, the computing platform 5300 maydetermine a likelihood that the stated income is faulty (or invalid)given the variables CREDIT_SCORE, CO, NCO, OTH, DELINQUENCIES, andINCOME_DIFF.

In one embodiment, the computing platform 5300 uses the followingequation to determine the odds, or likelihood that an outcome, such as afaulty stated income, is possible:

$\begin{matrix}{\left. {{Log}\mspace{14mu}\left( {{p/1} - p} \right)} \right) = {a + {b_{1}({CREDIT\_ SCORE})} + {b_{2}({CO})} + {b_{3}({NCO})} + {b_{4}({OTH})} + {b_{5}({DELINQUENCIES})} + {b_{6}({INCOME\_ DIFF})} + {\ldots\mspace{14mu}{b_{n\mspace{11mu}}\left( {n^{th}\mspace{14mu}{Variable}} \right)}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$where Log(p/(1−p)) represents the log odds (also referred to as LOGIT)that the stated income value is likely to be faulty; p represents theprobability of a borrower having a “0” outcome (or a “1” outcome); a,b₁, b₂, . . . b_(n) represent the initial coefficients of the IncomeScore model; and n represents the number of coefficients used in theIncome Score model, where b_(n) represents the n^(th) coefficient.Before the computing platform 5300 utilizes a logistic regression, thevalues of a, b₁, b₂, . . . b_(n), and p may be unknown.

In this example, the computing platform 1500 uses seven coefficients(i.e., n=6) corresponding to a, b₁, b₂, b₃, b₄, b₅, and b₆: Intercept,CREDIT_SCORE, CO, NCO, OTH, DELINQUENCIES, and INCOME_DIFF. The value“100” (line 1 of FIG. 9) corresponds to the variable “a” in Equation 2.The variable “a” is referred to herein as the Intercept that centers theIncome Score model distribution. For example, in an Income Score modelwith Income Scores between 1 and 10, the Intercept (a) may be selectedto center that model at 5. Although this example uses sevencoefficients, a skilled artisan would recognize that additional (orfewer) coefficients may be used instead.

Although p is an unknown value at the start of the logistic regression,p may conform to the following equation:p=1/(1+e ^(τ))  Equation 3where τ is the following:

$\begin{matrix}{\tau = {a + {b_{1}({CREDIT\_ SCORE})} + {b_{2}({CO})} + {b_{3}({NCO})} + {b_{4}({OTH})} + {b_{5}({DELINQUENCIES})} + {b_{6}({INCOME\_ DIFF})} + {\ldots\mspace{14mu} b_{n}\mspace{11mu}\left( {n^{th}\mspace{14mu}{Variable}} \right)}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$The computing platform 5300 may then determine an estimate of thecoefficients of the Income Score model (step 12400). That is, thecomputing platform 5300 may iteratively solve for an estimate of a, b1,b2 . . . b_(n) using Equations 2-4.

Although the computing platform 5300 may utilize a logistic regressionapproach as described in this example, a skilled artisan would recognizethat any other approach may be used instead to determine thecoefficients. Examples of such approaches include Probit regression,standard regression, neural networks, and any other statistical orquantitative approach that may provide model coefficients based onhistorical (or “truth”) information.

Referring again to FIG. 12, to adjust the coefficients (step 12500), thecomputing platform 5300 may then scale the coefficients a, b₁, b₂, . . .b_(n). In one embodiment, the computing platform 5300 may scale thecoefficients by multiplying each coefficient by the following equation:actual coefficient=initial coefficient*(60/ln(2))  Equation 5where ln is a natural logarithm.

By using Equation 5, the computing platform 5300 may scale the initialcoefficients such that every 60 Income Score points doubles the oddsthat a borrower's stated income is likely to be faulty. The scaledcoefficients may be used as the actual coefficients used in the IncomeScore model, such as the Income Score model illustrated in FIG. 9.Accordingly, the computing platform 5300 may determine coefficients forthe Income Score model based on a logistic regression approach usinghistorical (or “truth”) information. The computing platform 5300 maythen determine the Income Score based on the Income Score model.

The computing platform may then determine Income Scores for borrowersbased on the Income Score model. For example, for a borrower applyingfor a mortgage, the computing platform 5300 may receive information forthat borrower. In this example, the received information indicates acredit score (CREDIT_SCORE=of 700), a non-cash out mortgage (NCO=1), andno delinquencies in the borrower's credit history (DELINQUENCIES=0).Moreover, the borrower's stated income and income estimate are thesame—in this example $100,000. The difference in the stated andestimated incomes, in this case, is 0 based on Equation 1 above.Referring to the model depicted in FIG. 9, the borrower would have anIncome Score of 810.

In contrast, when another borrower applies for a mortgage, the computingplatform 5300 receives information for that borrower. In this example,all the received information is the same as the previous example (i.e.,CREDIT_SCORE=700, NCO=1, DELINQUENCIES=0). But in this case, althoughthe borrower's stated income is still $100,000, the income estimatereceived from an income estimation model suggests an income of $50,000.In essence, the income estimation model suggests that the borrower hasoverstated his income by $50,000. Referring again to the model depictedin FIG. 9, the borrower's Income Score is 300, which is scaled up from acalculated value of 116. The 300 score corresponds to a low Income Scorethat suggests a very high likelihood that the borrower's stated incomeis false.

Furthermore, although the embodiments above refer to processinginformation related to mortgages, in its broadest sense systems andmethods consistent with the present invention may provide an IncomeScore for any type of loan, credit instrument, or line of credit, or anyother purpose where knowledge of income is important (e.g. marketinglists). Moreover, the Income Score may be used in any type oftransaction where an assessment is made of a self-reported (or stated)income. For example, the Income Score may be used to assess thereliability of stated income for any type of transaction includingverifying income on a rental or lease agreement, preapproving creditcard applications, verifying stated income on a credit card application,and verifying income on an employment application.

For example, when a property owner leases an apartment, the propertyowner may verify the renter's income before entering into the leaseagreement. Systems and methods consistent with the present invention maybe used as part of this process to determine whether the renter's statedincome is likely to be faulty. A landlord (or management company) maythen decide whether to pursue an expensive comprehensive incomeverification process based on the Income Score.

A credit card issuer may also use the Income Score to determine thelikelihood that a credit card applicant's stated income is faulty. Basedon the Income Score, the credit card issuer can decide whether toapprove the credit card application immediately or upgrade to acomprehensive income verification process.

Another example of a transaction where the Income Score may be utilizedincludes assessing the likelihood that a job applicant's income history,as listed in the employment application, is likely to be faulty. Becausean employer may offer the job applicant a salary based on incomehistory, the employer has an interest in determining the likelihood thatthe applicant's stated income history is likely to be faulty.

Although the above description of income uses wage income, in itsbroadest sense systems and methods consistent with the present inventionmay provide an Income Score and an Income Score model that account forwage and nonwage income.

The systems disclosed herein may be embodied in various forms including,for example, a data processor, such as a computer that also includes adatabase. Moreover, the above-noted features and other aspects andprinciples of the present invention may be implemented in variousenvironments. Such environments and related applications may bespecially constructed for performing the various processes andoperations of the invention or they may include a general-purposecomputer or computing platform selectively activated or reconfigured bycode to provide the necessary functionality. The processes disclosedherein are not inherently related to any particular computer or otherapparatus, and may be implemented by a suitable combination of hardware,software, and/or firmware. For example, various general-purpose machinesmay be used with programs written in accordance with teachings of theinvention, or it may be more convenient to construct a specializedapparatus or system to perform the required methods and techniques.

Systems and methods consistent with the present invention also includecomputer readable media that include program instruction or code forperforming various computer-implemented operations based on the methodsand processes of the invention. The media and program instructions maybe those specially designed and constructed for the purposes of theinvention, or they may be of the kind well known and available to thosehaving skill in the computer software arts. Examples of programinstructions include, for example, machine code, such as produced by acompiler, and files containing a high level code that can be executed bythe computer using an interpreter.

1. A method performed by a computer for applying data to generate aprediction of whether a stated income is faulty or reliable, the methodcomprising steps of: the computer accessing income data reflectingstated incomes of the previous borrowers, wherein the previous borrowersinclude a first group of previous borrowers having reliable statedincomes and a second group of previous borrowers having faulty statedincomes; the computer processing the income data to identify theprevious borrowers in the first group and the previous borrowers in thesecond group; the computer accessing borrower data reflectingcharacteristics of the previous borrowers; the computer determiningpredictive values of the characteristics of the previous borrowers usingboth the characteristics of the previous borrowers in the first groupand the characteristics of the previous borrowers in the second group,wherein the predictive values represent correlations between thecharacteristics of the previous borrowers and accuracy of the statedincomes of the previous borrowers; the computer storing, in a memorydevice, data reflecting the predictive values; and the computer applyingthe data reflecting the predictive values to correspondingcharacteristics of a current borrower to generate a prediction ofwhether a stated income of the current borrower is faulty or reliable.2. The computer-implemented method of claim 1, wherein the stated incomeof the current borrower is provided by the current borrower.
 3. Thecomputer-implemented method of claim 1, wherein the stated income of thecurrent borrower is from a source other than the current borrower. 4.The computer-implemented method of claim 1, wherein the stated incomesof the previous borrowers are verified to confirm accuracy.
 5. Thecomputer-implemented method of claim 1, wherein the data reflecting thepredictive values comprise coefficients incorporated into a model, themodel using income estimate to provide a score for the current borrower.6. The computer-implemented method of claim 5, wherein the score isbased at least partly on a difference between the stated income of thecurrent borrower and the income estimate.
 7. The computer-implementedmethod of claim 6, further comprising the step of: defining thedifference based on the following equation:difference=(log(the stated income)−log(the income estimate))/Std devwherein Std dev represents a standard deviation associated with themodel, and log represents a natural logarithm.
 8. Thecomputer-implemented method of claim 1, wherein the data reflecting thepredictive values is used to verify stated incomes for a plurality ofcurrent borrowers.
 9. The computer-implemented method of claim 1,wherein the data reflecting the predictive values are incorporated intoa model that determines a score for the current borrower for each of aplurality of loans.
 10. The computer-implemented method of claim 1,further comprising: accessing verification income data for the previousborrowers reflecting verified incomes of the previous borrowers; andcomparing the stated incomes of the previous borrowers income valueswith the verified incomes of the previous borrowers to determine thepredictive values.
 11. The computer-implemented method of claim 10,further comprising determining a difference between the stated incomesof the previous borrowers and the verified incomes of the previousborrowers, such that when the difference exceeds about 10%, the statedincomes are considered invalid.
 12. The computer-implemented method ofclaim 1, wherein the predictive values are different for different typesof loans.
 13. The computer-implemented method of claim 12, furthercomprising determining a probability that the stated income of thecurrent borrower is faulty for each of the different types of loans. 14.The computer-implemented method of claim 13, further comprisingverifying the stated income of the current borrower, adding the statedincome of the current borrower to the income data reflecting statedincomes of the previous borrowers, and determining new predictive valuesto predict whether a stated income of a new borrower is likely to befaulty.
 15. The computer-implemented method of claim 14, wherein the newpredictive values comprise a plurality of coefficients calculated usinga statistical technique.
 16. The computer-implemented method of claim15, wherein the statistical technique incorporates logistic regressionto calculate the plurality of coefficients.
 17. The computer-implementedmethod of claim 16, wherein the plurality of coefficients are calculatedbased on whether the stated incomes of the previous borrowers aredetermined to be reliable.
 18. The computer-implemented method of claim17, wherein the coefficients are adjusted to reflect faulty statedincomes for one or more of the previous borrowers.
 19. Thecomputer-implemented method of claim 1, wherein the data reflecting thepredictive values comprises a plurality of coefficients calculated usinga neural network.
 20. The computer-implemented method of claim 1,wherein the data reflecting the predictive values comprises a pluralityof coefficients determined using a rules-based expert system.
 21. Thecomputer-implemented method of claim 1, further comprising the step of:providing a model incorporating the data reflecting the predictivevalues.
 22. The computer-implemented method of claim 21, wherein themodel is provided using the Internet.
 23. The computer-implementedmethod of claim 21, wherein the model is provided such that a lender mayuse the model to approve a mortgage loan.
 24. The computer-implementedmethod of claim 21, wherein the model is provided such that a lender mayuse the model to approve a credit card.
 25. The computer-implementedmethod of claim 1, wherein the data reflecting the predictive values areused to generate a score, and a low score corresponds to a highlikelihood that the stated income of the current borrower is faulty anda high score corresponds to a low likelihood that the stated income ofthe current borrower is faulty.
 26. The computer-implemented method ofclaim 25, wherein the score is within a range of about 300 to about 900.27. The computer-implemented method of claim 1, wherein the statedincome of the current borrower reflects a combined income of the currentborrower and one or more additional current borrowers.
 28. Thecomputer-implemented method of claim 27, wherein scores are provided forthe plurality of current borrowers and the scores are scaled to a range.29. The computer-implemented method of claim 28, wherein the range isfrom about 300 to about
 900. 30. The computer-implemented method ofclaim 1, wherein the data reflecting the predictive values are used togenerate a score, and a high score corresponds to a high likelihood thatthe stated income of the current borrower is faulty and a low scorecorresponds to a low likelihood that the stated income of the currentborrower is faulty.
 31. A system for applying data to generate aprediction of whether a stated income is faulty or reliable, the systemcomprising: means for accessing income data reflecting stated incomes ofprevious borrowers, wherein the previous borrowers include a first groupof previous borrowers having reliable stated incomes and a second groupof previous borrowers having faulty stated incomes; means for processingthe income data to identify the previous borrowers in the first groupand the previous borrowers in the second group; means for accessingborrower data reflecting characteristics of the previous borrowers;means for determining predictive values of the characteristics of theprevious borrowers using both the characteristics of the previousborrowers in the first group and the characteristics of the previousborrowers in the second group, wherein the predictive values representcorrelations between the characteristics of the previous borrowers andaccuracy of the stated incomes of the previous borrowers; a memorydevice configured to store data reflecting the predictive values; andmeans for applying the data reflecting the predictive values tocorresponding characteristics of a current borrower to generate aprediction of whether a stated income of the current borrower is faultyor reliable.
 32. A system for applying data to generate a prediction ofwhether a stated income is faulty or reliable, the system comprising: atleast one memory comprising computer executable code that: accessesincome data reflecting stated incomes of previous borrowers, wherein theprevious borrowers include a first group of previous borrowers havingreliable stated incomes and a second group of previous borrowers havingfaulty stated incomes; processes the income data to identify theprevious borrowers in the first group and the previous borrowers in thesecond group; accesses borrower data reflecting characteristics of theprevious borrowers; determines predictive values of the characteristicsof the previous borrowers using both the characteristics of the previousborrowers in the first group and the characteristics of the previousborrowers in the second group, wherein the predictive values representcorrelations between the characteristics of the previous borrowers andaccuracy of the stated incomes of the previous borrowers; stores datareflecting the predictive values; and applies the data reflecting thepredictive values to corresponding characteristics of a current borrowerto generate a prediction of whether a stated income of the currentborrower is faulty or reliable.
 33. The system of claim 32, wherein thestated income of the current borrower is provided by the currentborrower.
 34. The system of claim 32, wherein the stated income of thecurrent borrower is from a source other than the current borrower.
 35. Acomputer-readable non transitory storage medium comprising a stored setof computer-executable instructions for applying data to generate aprediction of whether a stated income is faulty or reliable which whenexecuted by a processor perform a method comprising: accessing incomedata reflecting stated incomes of previous borrowers, wherein theprevious borrowers include a first group of previous borrowers havingreliable stated incomes and a second group of previous borrowers havingfaulty stated incomes; processing, using the computing platform, theincome data to identify the previous borrowers in the first group andthe previous borrowers in the second group; accessing borrower datareflecting characteristics of the previous borrowers; determiningpredictive values of the characteristics of the previous borrowers usingboth the characteristics of the previous borrowers in the first groupand the characteristics of the previous borrowers in the second group,wherein the predictive values represent correlations between thecharacteristics of the previous borrowers and accuracy of the statedincomes of the previous borrowers; storing data reflecting thepredictive values; and applying the data reflecting the predictivevalues to corresponding characteristics of a current borrower togenerate a prediction of whether a stated income of the current borroweris faulty or reliable.